The preferences of saproxylic beetle species for different dead wood types created in forest restoration treatments
Bibliographic record
Abstract
Restoration by imitating natural disturbances is widely practised in boreal forests to increase the availability of habitats for specialized species. We studied the abundance and species richness of saproxylic beetles on different types of created dead wood during 2 years after restoration. The study was conducted on areas of a large-scale experiment in which Norway spruce ( Picea abies (L.) Karst.) forests were restored by controlled burning and partial harvesting with down wood retention in southern Finland. More beetle species were attracted to spruces than to birches and more species were attracted to burnt trees than to unburnt trees killed by girdling. Birch-living species consistently benefited from fire, but on spruce, the abundance of cambium consumers and their associates was negatively affected by fire. Trees at harvested sites attracted more beetles in the first year, but the volume of down wood retention had only minor effects. Beetle assemblages were strongly altered by burning and harvesting. We conclude that burning and harvesting are efficient tools to promote species richness within a short time period, but there is a risk that the dead wood resource may be rapidly exhausted. Moreover, many saproxylic species of spruce forests may not be adapted to open habitats formed by stand-replacing disturbances.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".